Merlise Clyde

Journal article Journal of the American Statistical Association · December 1, 2018 Featured Publication Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of g-priors to Generalized Linear Models (GLMs) ... Full text Open Access Link to item Cite

Redefine statistical significance.

Journal article Nature human behaviour · January 2018 Featured Publication Full text Open Access Cite

Rao-blackwellization for Bayesian variable selection and model averaging in linear and binary regression: A novel data augmentation approach

Journal article Journal of the American Statistical Association · October 21, 2011 Featured Publication Choosing the subset of covariates to use in regression or generalized linear models is a ubiquitous problem. The Bayesian paradigm addresses the problem of model uncertainty by considering models corresponding to all possible subsets of the covariates, whe ... Full text Cite

Stochastic expansions using continuous dictionaries: Lévy adaptive regression kernels

Journal article Annals of Statistics · 2011 Featured Publication This article describes a new class of prior distributions for nonparametric function estimation. The unknown function is modeled as a limit of weighted sums of kernels or generator functions indexed by continuous parameters that control local and global fe ... Full text Open Access Cite

Mixtures of g-priors for Bayesian Variable Selection

Journal article Journal of the American Statistical Association · 2008 Featured Publication Zellner's g prior remains a popular conventional prior for use in Bayesian variable selection, despite several undesirable consistency issues. In this article we study mixtures of g priors as an alternative to default g priors that resolve many of the prob ... Full text Link to item Cite